Grammatical error correction tools are effective at correcting grammatical
errors in users' input sentences but do not provide users with \textit{natural
language} explanations about their errors. Such explanations are essential for
helping users learn the language by gaining a deeper understanding of its
grammatical rules (DeKeyser, 2003; Ellis et al., 2006). To address this gap, we
propose the task of grammar error explanation, where a system needs to provide
one-sentence explanations for each grammatical error in a pair of erroneous and
corrected sentences. We analyze the capability of GPT-4 in grammar error
explanation, and find that it only produces explanations for 60.2% of the
errors using one-shot prompting. To improve upon this performance, we develop a
two-step pipeline that leverages fine-tuned and prompted large language models
to perform structured atomic token edit extraction, followed by prompting GPT-4
to generate explanations. We evaluate our pipeline on German and Chinese
grammar error correction data sampled from language learners with a wide range
of proficiency levels. Human evaluation reveals that our pipeline produces
93.9% and 98.0% correct explanations for German and Chinese data, respectively.
To encourage further research in this area, we will open-source our data and
code.

通过使用两步式流程，我们提出了一个用于语法错误解释的系统，该系统可以对每个语法错误提供一句解释，并通过分析 GPT-4 在语法错误解释方面的能力，提高了错误解释的准确性。我们的研究还评估了使用我们的系统处理德语和中文语法错误纠正数据的效果，并得出结论该系统可以高效地生成正确的解释，为德语和中文的学习者提供帮助。最后，我们决定开源我们的数据和代码，以鼓励该领域的进一步研究。